PROJECT SUMMARY/ABSTRACT This research program aims to bridge the gap between genomics data generation from clinical samples and our ability to infer and interpret intricate regulatory programs that underpin cell function and dysfunction in human cells. The Ucar laboratory develops and applies computational solutions to uncover complex regulatory programs in human cells and address previously inaccessible questions related to how disruptions in these programs affect human health and disease. The goal is to create computational tools that are versatile, easy to use and in keeping with the ever-increasing sophistication and complexity of NGS data. The current focus on the immunobiology of aging leverages the Principal Investigator's extensive training in computer science, epigenomics, and aging biology. Ongoing work with collaborators at The Jackson Laboratory and The University of Connecticut Health Center has led to multiple discoveries related to the genomic signatures of human immune aging, and has yielded numerous questions that form the basis for the proposed research program, including: 1) Which regulatory programs and regulatory interactions are disrupted with aging in which immune cells? 2) How do men and women age differently? 3) What are the putative genomic/clinical/immunological markers of healthy aging? To address these questions, this research program will focus on developing machine learning and network mining algorithms that enable integration of data from diverse sources, since complex regulatory interactions and diverse regulatory elements cannot be inferred from a single data type. Fueled by these tools, it will investigate the dynamics of regulatory programs in blood- derived human immune cells associated with aging through collaborations with clinicians, immunologists, and chromatin scientists. This research will advance our understanding of how immune responses are transcriptionally regulated, will facilitate the design of interventions to boost immune health in elderly and diseased individuals, and will yield computational resources useful to diverse areas of genomic medicine.